title: Applying science and mathematics to big data for smarter buildings.
abstract: Many buildings are now collecting a large amount of data on operations, energy consumption, and activities through systems such as a building management system (BMS), sensors, and meters (e.g., submeters and smart meters). However, the majority of data are not utilized and are thrown away. Science and mathematics can play an important role in utilizing these big data and accurately assessing how energy is consumed in buildings and what can be done to save energy, make buildings energy efficient, and reduce greenhouse gas (GHG) emissions. This paper discusses an analytical tool that has been developed to assist building owners, facility managers, operators, and tenants of buildings in assessing, benchmarking, diagnosing, tracking, forecasting, and simulating energy consumption in building portfolios.
authors.affiliation: IBM Thomas J. Watson Research Center, 1101 Kitchawan Road, Yorktown Heights, NY 10598, USA. ymlee@us.ibm.com
authors.forename: Young M
authors.initials: YM
authors.lastname: Lee
authors.forename: Lianjun
authors.initials: L
authors.lastname: An
authors.forename: Fei
authors.initials: F
authors.lastname: Liu
authors.forename: Raya
authors.initials: R
authors.lastname: Horesh
authors.forename: Young Tae
authors.initials: YT
authors.lastname: Chae
authors.forename: Rui
authors.initials: R
authors.lastname: Zhang
mesh_terms: D000397:Air Pollution / Q000517:prevention & control / Q000706:statistics & numerical data; D004463:Ecology / Q000379:methods*; D005160:Facility Design and Construction / Q000379:methods* / Q000706:statistics & numerical data; D017752:Greenhouse Effect / Q000517:prevention & control / Q000706:statistics & numerical data; D006801:Humans; D055641:Mathematical Concepts; D013223:Statistics as Topic / Q000379:methods*
keywords: analytics; buildings; data; energy; performance